LANGUAGE DETECTION USING MACHINE LEARNING

Abstract

This paper presents an innovative Machine Learning (ML) model for language detection that combines the power of logistic regression with a multimodal approach. The proposed model is designed to handle three types of inputs: sequential text data, files, and image representations. The proposed model offers a versatile and accurate solution for identifying languages across diverse data modalities. The model architecture employs logistic regression to enhance interpretability and feature extraction from each input modality. Trained on a comprehensive multilingual dataset, the model exhibits robust performance, showcasing its applicability to real-world scenarios. The model’s ability to process text, files, and images makes it well-suited for applications in content filtering, cross-modal information retrieval, and multilingual sentiment analysis. This research contributes to the advancement of language detection models by offering a unified solution for handling diverse input types.